diff --git a/M03_TP01_Solution.ipynb b/M03_TP01_Solution.ipynb new file mode 100644 index 0000000..366436a --- /dev/null +++ b/M03_TP01_Solution.ipynb @@ -0,0 +1,2045 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "55d19678", + "metadata": {}, + "source": [ + "# TP1 du module 3 : les librairies Python pour le ML\n", + "\n", + "Dans ce TP, nous allons explorer les quatre librairies présentées dans le cours. Objectifs :\n", + "* Savoir créer et récupérer des données avec Numpy et Pandas\n", + "* Savoir interroger les données avec Pandas\n", + "* Savoir calculer des statistiques de base\n", + "* Produire des visualisations avec Matplotlib et Seaborn" + ] + }, + { + "cell_type": "code", + "id": "4ceeadf3", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-15T13:33:29.589681Z", + "start_time": "2025-09-15T13:33:29.587631Z" + } + }, + "source": [ + "# Insérer ici l'import des librairies dont vous avez besoin\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns" + ], + "outputs": [], + "execution_count": 1 + }, + { + "cell_type": "markdown", + "id": "06617af8", + "metadata": {}, + "source": [ + "## Première partie : s'échauffer avec Numpy\n", + "Commencons avec quelques exercices sur Numpy, qui vous permettront également de réviser certaines fonctionnalités de Python. Pour rappel, toute la documentation Numpy est disponible en cliquant sur ce [lien](https://numpy.org/doc/stable/user/index.html)\n", + "\n", + "**Question 1 :** créer une matrice `mat` 3*3 contenant tous les chiffres de 1 à 9, à l'aide des méthodes `arange` et `reshape`." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "f4bbe51d", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1 2 3]\n", + " [4 5 6]\n", + " [7 8 9]]\n" + ] + } + ], + "source": [ + "mat = np.arange(1, 10).reshape((3, 3))\n", + "print(mat)" + ] + }, + { + "cell_type": "markdown", + "id": "b7a6cbc4", + "metadata": {}, + "source": [ + "**Question 2 :** écrire une fonction prenant en paramètre une matrice carré (de taille n*n, où n est un entier), et qui renvoie sa trace (la somme des nombres sur la diagonale de la matrice), sans utiliser la méthode `trace` de numpy. Appliquez votre fonction à la matrice `mat` créée à la question précédente, et comparez le résultat avec la méthode `trace` de numpy pour valider votre fonction." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f646cc15", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Avec la fonction écrite en Python : 15\n", + "Avec la méthode numpy : 15\n" + ] + } + ], + "source": [ + "def trace(matrice):\n", + " trace =0\n", + " for i in range(0, len(matrice)) :\n", + " trace+=matrice[i][i]\n", + " \n", + " return trace\n", + "\n", + "print(\"Avec la fonction écrite en Python : \", trace(mat))\n", + "print(\"Avec la méthode numpy : \", np.trace(mat))" + ] + }, + { + "cell_type": "markdown", + "id": "05c9d50e", + "metadata": {}, + "source": [ + "**Question 3 :** créez une deuxième matrice `mat2`, de dimension 3*3 également, qui ne contient que des 1." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "d09bc99f", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1. 1. 1.]\n", + " [1. 1. 1.]\n", + " [1. 1. 1.]]\n" + ] + } + ], + "source": [ + "mat2 = np.ones((3,3))\n", + "print(mat2)" + ] + }, + { + "cell_type": "markdown", + "id": "81406a80", + "metadata": {}, + "source": [ + "**Question 4 :** créez la matrice `vert`, qui consiste à empiler verticalement `mat` sur `mat2`. Résultat attendu :\n", + "\n", + "| | | |\n", + "|---|---|---|\n", + "| 1 | 2 | 3 |\n", + "| 4 | 5 | 6 |\n", + "| 7 | 8 | 9 |\n", + "| 1 | 1 | 1 |\n", + "| 1 | 1 | 1 |\n", + "| 1 | 1 | 1 |" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "e79b59fd", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1. 2. 3.]\n", + " [4. 5. 6.]\n", + " [7. 8. 9.]\n", + " [1. 1. 1.]\n", + " [1. 1. 1.]\n", + " [1. 1. 1.]]\n" + ] + } + ], + "source": [ + "vert = np.vstack((mat, mat2))\n", + "print(vert)" + ] + }, + { + "cell_type": "markdown", + "id": "4d168af1", + "metadata": {}, + "source": [ + "**Question 5 :** créez la matrice `hori`, qui consiste à coller horizontalement `mat` à `mat2`. Résultat attendu :\n", + "\n", + "| | | | | | |\n", + "|---|---|---|---|---|---|\n", + "| 1 | 2 | 3 | 1 | 1 | 1 |\n", + "| 4 | 5 | 6 | 1 | 1 | 1 |\n", + "| 7 | 8 | 9 | 1 | 1 | 1 |" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "041d438b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1. 2. 3. 1. 1. 1.]\n", + " [4. 5. 6. 1. 1. 1.]\n", + " [7. 8. 9. 1. 1. 1.]]\n" + ] + } + ], + "source": [ + "hori = np.hstack((mat, mat2))\n", + "print(hori)" + ] + }, + { + "cell_type": "markdown", + "id": "c37d28f6", + "metadata": {}, + "source": [ + "**Question 5 :** écrire une fonction qui prend en paramètre une matrice et un nombre, et qui renvoie le nombre d'éléments de la matrice qui sont strictement inférieurs à ce nombre. Vous pouvez utiliser la méthode `count_nonzero` de numpy." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f916be17", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Nombre de valeurs strictement inférieures à 2 dans la matrice hori : 10\n" + ] + } + ], + "source": [ + "def less_than(matrice, limit):\n", + " return np.count_nonzero(matrice < limit)\n", + "\n", + "print(\"Nombre de valeurs strictement inférieures à 2 dans la matrice hori : \", less_than(hori, 2))" + ] + }, + { + "cell_type": "markdown", + "id": "a6a6b43b", + "metadata": {}, + "source": [ + "## Partie 2 : manipuler un jeu de données\n", + "Après cet échauffement, nous allons passer à la manipulation d'un jeu de données, pour en commencer l'analyse, et pour produire des visualisations.\n", + "\n", + "### Présentation du jeu de données\n", + "Pour ce TP (et les suivants), nous allons analyser les données d'un jeu de données sur les passagers du Titanic. Pour rappel, le RMS Titanic est un paquebot transatlantique britannique qui fait naufrage dans l'océan Atlantique Nord en 1912 à la suite d'une collision avec un iceberg, lors de son voyage inaugural de Southampton à New York. Entre 1 490 et 1 520 personnes trouvent la mort, ce qui fait de cet événement l'une des plus grandes catastrophes maritimes survenues en temps de paix et la plus grande pour l'époque. \n", + "\n", + "![Le Titanic à Southampton le 10 avril 1912. ](https://upload.wikimedia.org/wikipedia/commons/thumb/f/fd/RMS_Titanic_3.jpg/420px-RMS_Titanic_3.jpg)\n", + "\n", + "Ce jeu de données est assez utilisé pour débuter dans le machine learning. Nous irons plus loin sur le prochain TP (module 4) dans son analyse, sur cette partie l'objectif est simplement de prendre en main Pandas, Matplotlib et Seaborn.\n", + "\n", + "Les données contiennent un ensemble d'information sur ces passages (genre, age, etc), ainsi qu'un atribut sépcifiant s'ils ont ou non survécu au naufrage. Pour vous aider, voilà un bref descriptifs des attributs du jeu de données : \n", + "\n", + "| Attribut | Explication | \n", + "| ------------- |:-------------: | \n", + "| Survived | 0 = Décédé, 1 = Survivant| \n", + "| PClass | 1 = cabine 1ere classe, 2= cabine 2e classe, 3=cabine 3e classe | \n", + "| Sex | 0=femme, 1=homme |\n", + "| sibsp | Nombre de frères, soeurs, et/ou conjoint à bord |\n", + "|parch | Nombre de parents et/ou enfants à bord |\n", + "| ticket | Numéro du ticket |\n", + "|cabin | Numéro de cabine |\n", + "|embarked | Port d'embarquement (C = Cherbourg; Q = Queenstown; S = Southampton) |" + ] + }, + { + "cell_type": "markdown", + "id": "5e1c586f", + "metadata": {}, + "source": [ + "### Pandas\n", + "Dans cette partie, chaque question ne nécessite qu'une seule ligne de code. \n", + "\n", + "**Question 1 :** charger le jeu de données dans un Dataframe depuis l'adresse suivante : https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv" + ] + }, + { + "cell_type": "code", + "id": "96ab19ad", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-15T13:45:14.092698Z", + "start_time": "2025-09-15T13:45:13.993011Z" + } + }, + "source": [ + "titanic = pd.read_csv('https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv')\n", + "titanic" + ], + "outputs": [ + { + "data": { + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + ".. ... ... ... \n", + "886 887 0 2 \n", + "887 888 1 1 \n", + "888 889 0 3 \n", + "889 890 1 1 \n", + "890 891 0 3 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "2 Heikkinen, Miss. Laina female 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", + "4 Allen, Mr. William Henry male 35.0 0 \n", + ".. ... ... ... ... \n", + "886 Montvila, Rev. Juozas male 27.0 0 \n", + "887 Graham, Miss. Margaret Edith female 19.0 0 \n", + "888 Johnston, Miss. Catherine Helen \"Carrie\" female NaN 1 \n", + "889 Behr, Mr. Karl Howell male 26.0 0 \n", + "890 Dooley, Mr. Patrick male 32.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked \n", + "0 0 A/5 21171 7.2500 NaN S \n", + "1 0 PC 17599 71.2833 C85 C \n", + "2 0 STON/O2. 3101282 7.9250 NaN S \n", + "3 0 113803 53.1000 C123 S \n", + "4 0 373450 8.0500 NaN S \n", + ".. ... ... ... ... ... \n", + "886 0 211536 13.0000 NaN S \n", + "887 0 112053 30.0000 B42 S \n", + "888 2 W./C. 6607 23.4500 NaN S \n", + "889 0 111369 30.0000 C148 C \n", + "890 0 370376 7.7500 NaN Q \n", + "\n", + "[891 rows x 12 columns]" + ], + "text/html": [ + "
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88688702Montvila, Rev. Juozasmale27.00021153613.0000NaNS
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" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 4 + }, + { + "cell_type": "markdown", + "id": "539c23f2", + "metadata": {}, + "source": [ + "**Question 2 :** combien de lignes comporte ce dataset ? Combien de colonnes ? Contient-il des valeurs nulles dans certaines colonnes ?" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "72c8a945", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 891 entries, 0 to 890\n", + "Data columns (total 12 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 PassengerId 891 non-null int64 \n", + " 1 Survived 891 non-null int64 \n", + " 2 Pclass 891 non-null int64 \n", + " 3 Name 891 non-null object \n", + " 4 Sex 891 non-null object \n", + " 5 Age 714 non-null float64\n", + " 6 SibSp 891 non-null int64 \n", + " 7 Parch 891 non-null int64 \n", + " 8 Ticket 891 non-null object \n", + " 9 Fare 891 non-null float64\n", + " 10 Cabin 204 non-null object \n", + " 11 Embarked 889 non-null object \n", + "dtypes: float64(2), int64(5), object(5)\n", + "memory usage: 83.7+ KB\n", + "None\n" + ] + } + ], + "source": [ + "print(titanic.info())" + ] + }, + { + "cell_type": "markdown", + "id": "292c712a", + "metadata": {}, + "source": [ + "**Question 3 :** calculez les statistiques de base sur les colonnes numériques ce jeu de données" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "3b8aea3e", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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313211Spencer, Mrs. William Augustus (Marie Eugenie)femaleNaN10PC 17569146.5208B78C
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" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "31 32 1 1 \n", + "\n", + " Name Sex Age SibSp Parch \\\n", + "31 Spencer, Mrs. William Augustus (Marie Eugenie) female NaN 1 0 \n", + "\n", + " Ticket Fare Cabin Embarked \n", + "31 PC 17569 146.5208 B78 C " + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic[titanic['Cabin']=='B78']" + ] + }, + { + "cell_type": "markdown", + "id": "3c6ac11b", + "metadata": {}, + "source": [ + "**Question 6 :** Sélectionnez uniquement les passagers âgés de plus de 30 ans (strictement), qui sont des femmes, qui voyageaient en première classe, et qui n'ont pas survécu.\n", + "\n", + "Résultat attendu :\n", + "\n", + "| | PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked |\n", + "|----:|------------:|---------:|-------:|---------------------------:|-------:|-----:|------:|------:|---------:|--------:|------:|---------:|\n", + "| 177 | 178 | 0 | 1 | Isham, Miss. Ann Elizabeth | female | 50.0 | 0 | 0 | PC 17595 | 28.7125 | C49 | C |" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "a7da8b14", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
17717801Isham, Miss. Ann Elizabethfemale50.000PC 1759528.7125C49C
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" + ], + "text/plain": [ + " PassengerId Survived Pclass Name Sex Age \\\n", + "177 178 0 1 Isham, Miss. Ann Elizabeth female 50.0 \n", + "\n", + " SibSp Parch Ticket Fare Cabin Embarked \n", + "177 0 0 PC 17595 28.7125 C49 C " + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic[(titanic['Sex']=='female') & (titanic['Age']>30) & (titanic['Pclass']==1) & (titanic['Survived']==0)]" + ] + }, + { + "cell_type": "markdown", + "id": "5fbf471a", + "metadata": {}, + "source": [ + "**Question 7 :** trouver le nom des passagers qui sont montés à Cherbourg ou à Queenstown, qui voyageaient en 1e classe, avec au moins deux frère/soeurs/conjoints? Résultat attendu :\n", + "\n", + "| | PassengerId | Survived | Pclass | Name | Sex | Age | SibSp | Parch | Ticket | Fare | Cabin | Embarked |\n", + "|----:|------------:|---------:|-------:|--------------------------------------:|-------:|-----:|------:|------:|---------:|--------:|----------------:|---------:|\n", + "| 245 | 246 | 0 | 1 | Minahan, Dr. William Edward | male | 44.0 | 2 | 0 | 19928 | 90.000 | C78 | Q |\n", + "| 311 | 312 | 1 | 1 | Ryerson, Miss. Emily Borie | female | 18.0 | 2 | 2 | PC 17608 | 262.375 | B57 B59 B63 B66 | C |\n", + "| 742 | 743 | 1 | 1 | Ryerson, Miss. Susan Parker \"Suzette\" | female | 21.0 | 2 | 2 | PC 17608 | 262.375 | B57 B59 B63 B66 | C |" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "eb6cf581", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
24524601Minahan, Dr. William Edwardmale44.0201992890.000C78Q
31131211Ryerson, Miss. Emily Boriefemale18.022PC 17608262.375B57 B59 B63 B66C
74274311Ryerson, Miss. Susan Parker \"Suzette\"female21.022PC 17608262.375B57 B59 B63 B66C
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" + ], + "text/plain": [ + " PassengerId Survived Pclass Name \\\n", + "245 246 0 1 Minahan, Dr. William Edward \n", + "311 312 1 1 Ryerson, Miss. Emily Borie \n", + "742 743 1 1 Ryerson, Miss. Susan Parker \"Suzette\" \n", + "\n", + " Sex Age SibSp Parch Ticket Fare Cabin Embarked \n", + "245 male 44.0 2 0 19928 90.000 C78 Q \n", + "311 female 18.0 2 2 PC 17608 262.375 B57 B59 B63 B66 C \n", + "742 female 21.0 2 2 PC 17608 262.375 B57 B59 B63 B66 C " + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic[((titanic['Embarked']=='Q') | (titanic['Embarked']=='C')) & ((titanic['Pclass']==1) & (titanic['SibSp']>=2))]" + ] + }, + { + "cell_type": "markdown", + "id": "ec2f5672", + "metadata": {}, + "source": [ + "**Question 8 :** compter le nombre de femmes et d'hommes ayant survécu. (rappel : en une seule ligne de code !). Résultat attendu :\n", + "\n", + "| Sex | |\n", + "|-------------:|----:|\n", + "| female | 233 |\n", + "| male | 109 |\n", + "| dtype: int64 | |" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "bfeb02c2", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Sex\n", + "female 233\n", + "male 109\n", + "dtype: int64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic[titanic['Survived']==1].groupby('Sex').size()" + ] + }, + { + "cell_type": "markdown", + "id": "48f48106", + "metadata": {}, + "source": [ + "**Question 9** : pour chaque classe, calculer la moyenne d'âge. Résultat attendu :\n", + "\n", + "| Pclass | |\n", + "|:-------------------------:|----------:|\n", + "| 1 | 38.233441 |\n", + "| 2 | 29.877630 |\n", + "| 3 | 25.140620 |\n", + "| Name: Age, dtype: float64 | |" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "bd7b6d5c", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Pclass\n", + "1 38.233441\n", + "2 29.877630\n", + "3 25.140620\n", + "Name: Age, dtype: float64" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic.groupby('Pclass')['Age'].mean()" + ] + }, + { + "cell_type": "markdown", + "id": "78cd99eb", + "metadata": {}, + "source": [ + "**Question 10 :** ajouter une colonne au datframe contenant les données du titanic, qui représente le nombre total de personnes accompagnant chaque passager (somme de `SibSp` et `Parch`)." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "722450a8", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarkedFamilyMembers
0103Braund, Mr. Owen Harrismale22.010A/5 211717.2500NaNS1
1211Cumings, Mrs. John Bradley (Florence Briggs Th...female38.010PC 1759971.2833C85C1
2313Heikkinen, Miss. Lainafemale26.000STON/O2. 31012827.9250NaNS0
3411Futrelle, Mrs. Jacques Heath (Lily May Peel)female35.01011380353.1000C123S1
4503Allen, Mr. William Henrymale35.0003734508.0500NaNS0
..........................................
88688702Montvila, Rev. Juozasmale27.00021153613.0000NaNS0
88788811Graham, Miss. Margaret Edithfemale19.00011205330.0000B42S0
88888903Johnston, Miss. Catherine Helen \"Carrie\"femaleNaN12W./C. 660723.4500NaNS3
88989011Behr, Mr. Karl Howellmale26.00011136930.0000C148C0
89089103Dooley, Mr. Patrickmale32.0003703767.7500NaNQ0
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891 rows × 13 columns

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" + ], + "text/plain": [ + " PassengerId Survived Pclass \\\n", + "0 1 0 3 \n", + "1 2 1 1 \n", + "2 3 1 3 \n", + "3 4 1 1 \n", + "4 5 0 3 \n", + ".. ... ... ... \n", + "886 887 0 2 \n", + "887 888 1 1 \n", + "888 889 0 3 \n", + "889 890 1 1 \n", + "890 891 0 3 \n", + "\n", + " Name Sex Age SibSp \\\n", + "0 Braund, Mr. Owen Harris male 22.0 1 \n", + "1 Cumings, Mrs. John Bradley (Florence Briggs Th... female 38.0 1 \n", + "2 Heikkinen, Miss. Laina female 26.0 0 \n", + "3 Futrelle, Mrs. Jacques Heath (Lily May Peel) female 35.0 1 \n", + "4 Allen, Mr. William Henry male 35.0 0 \n", + ".. ... ... ... ... \n", + "886 Montvila, Rev. Juozas male 27.0 0 \n", + "887 Graham, Miss. Margaret Edith female 19.0 0 \n", + "888 Johnston, Miss. Catherine Helen \"Carrie\" female NaN 1 \n", + "889 Behr, Mr. Karl Howell male 26.0 0 \n", + "890 Dooley, Mr. Patrick male 32.0 0 \n", + "\n", + " Parch Ticket Fare Cabin Embarked FamilyMembers \n", + "0 0 A/5 21171 7.2500 NaN S 1 \n", + "1 0 PC 17599 71.2833 C85 C 1 \n", + "2 0 STON/O2. 3101282 7.9250 NaN S 0 \n", + "3 0 113803 53.1000 C123 S 1 \n", + "4 0 373450 8.0500 NaN S 0 \n", + ".. ... ... ... ... ... ... \n", + "886 0 211536 13.0000 NaN S 0 \n", + "887 0 112053 30.0000 B42 S 0 \n", + "888 2 W./C. 6607 23.4500 NaN S 3 \n", + "889 0 111369 30.0000 C148 C 0 \n", + "890 0 370376 7.7500 NaN Q 0 \n", + "\n", + "[891 rows x 13 columns]" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "titanic['FamilyMembers'] = titanic['SibSp'] + titanic['Parch']\n", + "titanic" + ] + }, + { + "cell_type": "markdown", + "id": "10c01d50", + "metadata": {}, + "source": [ + "### Matplotlib\n", + "Il est à présent temps de proposer quelques visualisations sur ce jeu de données. Pour les questions suivantes, il vous est demandé d'utiliser uniquement matplotlib (pyplot), même si des équivalences peuvent exister avec seaborn.\n", + "\n", + "La documentation matplotlib est accessible à l'adresse suivante : https://matplotlib.org/stable/index.html\n", + "\n", + "**Question 1 :** afficher un graphe, de type scatterplot, affichant l'âge des passagers en fonction du prix de leur ticket. Pensez à bien afficher un texte sur chaque axe, et ajouter un titre." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "34f1fe70", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, \"Prix en fonction de l'âge\")" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(titanic['Age'], titanic['Fare'], marker='o', color=\"b\", linestyle='') \n", + "plt.ylabel(\"Prix du billet\") \n", + "plt.xlabel(\"Age du passager\")\n", + "plt.title(\"Prix en fonction de l'âge\")" + ] + }, + { + "cell_type": "markdown", + "id": "92a0c112", + "metadata": {}, + "source": [ + "**Question 2 :** afficher un histogramme (barplot) montrant, pour chaque classe (1e, 2e et 3e), le nombre de passagers à bord du bateau. Pensez à nommer vos axes et votre figure. Sur l'abscisse, seules les classes 1, 2 et 3 doivent apparaitre (pas de nombre décimal) : si nécessaire, regarder dans la documention comment utiliser `xticks`." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "b5c4552f", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Répartition des passagers dans chaque classe')" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "nb_passagers_par_classe = titanic[['Pclass']].groupby('Pclass').size()\n", + "plt.bar(nb_passagers_par_classe.index, nb_passagers_par_classe.values)\n", + "plt.xlabel('Classe des passagers')\n", + "plt.ylabel('Nombre de passagers')\n", + "plt.xticks(nb_passagers_par_classe.index)\n", + "plt.title('Répartition des passagers dans chaque classe')" + ] + }, + { + "cell_type": "markdown", + "id": "6f811c0d", + "metadata": {}, + "source": [ + "**Question 3 :** proposez un graphique similaire au précédent, en représentant séparement pour chaque classe le nombre d'hommes et le nombre de femmes, sur deux barres côte à côte. Pensez à nommer vos axes, votre figure, et à afficher une légende expliquant quelle couleur représente les hommes, et laquelle représente les femmes." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "5e32ab09", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "nb_passagers_par_classe_par_genre = titanic.groupby(['Sex', 'Pclass']).size()\n", + "data_f = nb_passagers_par_classe_par_genre['female']\n", + "data_m = nb_passagers_par_classe_par_genre['male']\n", + "\n", + "labels = data_f.index\n", + "pos = list(range(len(labels)))\n", + "width = 0.25\n", + "\n", + "plt.bar(pos, data_f, width, color='g', label='Femmes')\n", + "plt.bar([p+width for p in pos], data_m, width, color='y', label='Hommes')\n", + "\n", + "plt.xticks([p+width for p in pos], labels)\n", + "plt.xlabel('Classe des passagers')\n", + "plt.ylabel('Nombre de passagers')\n", + "plt.title('Répartition des passagers par genre et par classe')\n", + "plt.legend()\n" + ] + }, + { + "cell_type": "markdown", + "id": "aed1e423", + "metadata": {}, + "source": [ + "**Question 4 :** proposez un scatterplot montrant le nombre total de membres de la famille voyageant avec le passsagers (calculé à la question 10 de la section sur pandas), en fonction du prix du ticket (sur l'axe y, ordonnée). Colorez chaque point du graphique en fonction de si le passager a survécu ou non. Affichez une légende expliquant ce code couleur, donnez un titre à vos axes, et ajoutez un titre à votre graphique." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "ab3e1e93", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Nombre de membres de la famille en fonction du prix du ticket')" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.scatter(titanic[titanic['Survived']==0]['FamilyMembers'], titanic[titanic['Survived']==0]['Fare'], label='Non survécu')\n", + "plt.scatter(titanic[titanic['Survived']==1]['FamilyMembers'], titanic[titanic['Survived']==1]['Fare'], label='Survécu')\n", + "\n", + "plt.xlabel('Nombre de membres de la famille à bord')\n", + "plt.ylabel('Prix du ticket')\n", + "plt.legend()\n", + "plt.title('Nombre de membres de la famille en fonction du prix du ticket')" + ] + }, + { + "cell_type": "markdown", + "id": "35d5fe49", + "metadata": {}, + "source": [ + "**Question 5 :** proposez une figure composée de six sous-graphes, répartis sur deux lignes et 3 colonnes. Chaque colonne représente un port d'embarquement. Sur la première, pour chaque port, représentez sur un scatterplot l'âge des passagers (abscisse) en fonction du prix de leur billet (ordonnée). Sur la deuxième ligne, pour chaque port, représentez un barplot représentant le nombre de passagers de chaque genre." + ] + }, + { + "cell_type": "code", + "id": "1f3662f1", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-15T14:11:14.119153Z", + "start_time": "2025-09-15T14:11:13.423958Z" + } + }, + "source": [ + "titanic_cherbourg = titanic[titanic['Embarked']=='C']\n", + "titanic_southampton = titanic[titanic['Embarked']=='S']\n", + "titanic_queenstown = titanic[titanic['Embarked']=='Q']\n", + "\n", + "fig, axes = plt.subplots(2, 3, figsize=(10,10)) #2 lignes, 3 colonnes sur la figure\n", + "\n", + "# Première ligne première colonne\n", + "sub1 = axes[0,0]\n", + "sub1.scatter(titanic_cherbourg['Age'], titanic_cherbourg['Fare'])\n", + "sub1.set_ylabel('Prix du billet') # Titre des ordonnées pour la première ligne\n", + "sub1.set_title('Cherbourg') # Titre du sous-graphique\n", + "\n", + "# Première ligne deuxième colonne\n", + "sub2 = axes[0,1]\n", + "sub2.scatter(titanic_southampton['Age'], titanic_southampton['Fare'])\n", + "sub2.set_title('Southampton') \n", + "sub2.set_xlabel('Age des passagers')# Titre des abscisses pour la première ligne\n", + "\n", + "# Première ligne troisième colonne\n", + "sub3 = axes[0,2]\n", + "sub3.scatter(titanic_queenstown['Age'], titanic_queenstown['Fare'])\n", + "sub3.set_title('Queenstown')\n", + "\n", + "# Deuxième ligne première colonne\n", + "sub4 = axes[1,0]\n", + "sub4.bar(titanic_cherbourg[['Sex']].groupby('Sex').size().index, titanic_cherbourg[['Sex']].groupby('Sex').size().values)\n", + "sub4.set_ylabel('Nombre de passagers par sexe')\n", + "\n", + "# Deuxième ligne deuxième colonne\n", + "sub5 = axes[1,1]\n", + "sub5.bar(titanic_cherbourg[['Sex']].groupby('Sex').size().index, titanic_cherbourg[['Sex']].groupby('Sex').size().values)\n", + "sub5.set_xlabel('Genre')\n", + "\n", + "# Troisième ligne troisième colonne\n", + "sub6 = axes[1,2]\n", + "sub6.bar(titanic_cherbourg[['Sex']].groupby('Sex').size().index, titanic_cherbourg[['Sex']].groupby('Sex').size().values)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 5 + }, + { + "cell_type": "markdown", + "id": "d2845c23", + "metadata": {}, + "source": [ + "### Seaborn\n", + "\n", + "Vous allez cette fois générer des visualisations uniquement avec Seaborn. La documentation est accessible à l'adresse suivante : https://seaborn.pydata.org/api.html\n", + "\n", + "\n", + "**Question 1 :** proposez un scatterplot de l'âge des passagers en fonction du prix de leur ticket. Colorez chaque point en fonction du port dans lequel le passager à embarqué. Affichez une légende, nommez vos axes, et affichez un titre pour votre graphique." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "0068c9f7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[Text(0.5, 1.0, 'Age en fonction du prix du billet')]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.scatterplot(data=titanic, x='Age', y='Fare', hue='Embarked').set(title='Age en fonction du prix du billet')" + ] + }, + { + "cell_type": "markdown", + "id": "565aad2c", + "metadata": {}, + "source": [ + "**Question 2 :** faire un barplot représentant le nombre de passagers par genre. Indice : utilisez un `histplot`." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "c8cb3730", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.histplot(data=titanic, x='Sex');" + ] + }, + { + "cell_type": "markdown", + "id": "a12b94fb", + "metadata": {}, + "source": [ + "**Question 3 :** reproduire l'histogramme de la question 3 de la partie matplot lib - histogramme du nombre et de femmes pour chaque classe." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "f58d3ff7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.histplot(data=titanic, x='Pclass', hue='Sex', multiple='dodge')\n", + "plt.xticks" + ] + }, + { + "cell_type": "markdown", + "id": "5dbf5040", + "metadata": {}, + "source": [ + "**Question 4 :** proposer un pairplot pour les colonnes suivantes : Age et Fare. Colorer chaque point en fonction de si le passager a survécu ou non." + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "2cfc979b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.pairplot(titanic[['Age', 'Fare', 'Survived']], hue='Survived')" + ] + }, + { + "cell_type": "markdown", + "id": "8a3e5008", + "metadata": {}, + "source": [ + "**Question 5 :** en utilisant la fonction `corr` de pandas, et la fonction `heatmap` de seaborn, proposez une *heatmap* représentant la corrélation entre les attributs suivants : Survived, Age, Pclass, FamilyMembers(calculé par vos soins dans la partie pandas), et Fare." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "862165a1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.heatmap(titanic[['Survived', 'Age', 'Pclass', 'FamilyMembers', 'Fare']].corr())" + ] + }, + { + "cell_type": "markdown", + "id": "e0735b47", + "metadata": {}, + "source": [ + "# Fin du TP !" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.16" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/M04_TP01_Exercice.ipynb b/M04_TP01_Exercice.ipynb new file mode 100644 index 0000000..c4d6636 --- /dev/null +++ b/M04_TP01_Exercice.ipynb @@ -0,0 +1,441 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "395d61ec", + "metadata": {}, + "source": [ + "# TP1 du module 4 : le travail sur les données.\n", + "\n", + "Dans ce TP, nous allons explorer plus en détails le jeu de données du Titanic. Objectifs :\n", + "* Analyser des statistiques pour décrire les données\n", + "* Produire des visualisations pertinentes pour la compréhesion des données.\n", + "* Nettoyer le jeu de données\n", + "* Préparer les données pour qu'elles soient prêtes à être fournies à un algorithme d'apprentissage." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5117092f", + "metadata": {}, + "outputs": [], + "source": [ + "# Ajoutez ici les imports de librairies nécessaires\n" + ] + }, + { + "cell_type": "markdown", + "id": "fde8da96", + "metadata": {}, + "source": [ + "Commencez par recharger le jeu de données depuis un csv dans un dataframe Pandas. Rappel de l'adresse à laquelle vous pouvez le trouver : https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "33fba6ca", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "205f765d", + "metadata": {}, + "source": [ + "## Exploration du jeu de données\n", + "\n", + "Commencez par répondre au question suivante. Prenez le temps de bien analyser vos réponses, afin de mieux vous familiariser avec le contenu du jeu de données.\n", + "\n", + "1. Combien de données dans le jeu de données Titanic ?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ee3884e", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "74f32328", + "metadata": {}, + "source": [ + "2. Combien d'attributs compte le jeu de données ?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80eeccc3", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "819573a7", + "metadata": {}, + "source": [ + "3. Identifiez quelles colonnes contiennent des données discrètes, et lesquelles contiennent des données continues." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87aa38a1", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "59733c69", + "metadata": {}, + "source": [ + "4. De la même manière, identifiez les colonnes de données qualitatives, et celles de données quantitatives." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "67230396", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "69c4bee2", + "metadata": {}, + "source": [ + "5. Affichez les statistiques de base sur les colonnes quantitatives du dataset. \n", + "Quelles informations pouvez-vous en retirer ? Pour chaque attribut, cherchez au moins une information pertinente que vous pouvez déduire de vos observations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "82ebfbb6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "46384afa", + "metadata": {}, + "source": [ + "6. Sur une figure avec 6 sous-figures, proposez un histogramme pour visualiser la répartition des valeurs sur les attributs suivants : Survived, Pclass, Sex, Embarked, Age, Fare. Pour chaque figure, quelle(s) observation(s) pouvez-vous faire ?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "94ddbac5", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "620fbbba", + "metadata": {}, + "source": [ + "7. Sur un même graphique, représentez, pour chaque genre, le nombre de personnes ayant survécu. Que pouvez-vous en déduire ? Le genre d'un passager vous parait-il pertinent pour qu'un modèle d'apprentissage puisse prédire si ce passager a survécu ?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f58cb499", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "412e2e95", + "metadata": {}, + "source": [ + "8. En vous basant sur une visualisation, observez-vous une corrélation entre certains attributs ? Que pouvez-vous en déduire pour un futur modèle d'apprentissage ?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "48d8ee62", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "ae39b80c", + "metadata": {}, + "source": [ + "9. En vous basant sur vos observations de la visualisation précédente, confirmez vos impressions en proposant deux visualisations. Par exemple, si vous avez observé une corrélation entre un attribut A et un attribut B, mettez en valeur le fait que les mêmes valeurs de A sont souvent trouvées avec les mêmes valeurs de B. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "987a4b5f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9d66606a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "67adc89f", + "metadata": {}, + "source": [ + "10. Mettez-vous à présent dans le rôle d'un data analyst face à un nouveau jeu de données : quelles autres visualisations vous paraissent nécessaires ? Proposez au moins trois visualisations qui vous semblent pertinentes. Gardez en tête que l'objectif sur ce jeu de données sera de réussir à prédire si un passager à survécu ou non." + ] + }, + { + "cell_type": "markdown", + "id": "081aafe5", + "metadata": {}, + "source": [ + "## Nettoyage des données\n", + "1. Pour chaque colonne, comptez le nombre de valeurs nulles." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ba42c62a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "e5bc166d", + "metadata": {}, + "source": [ + "### Traitement du port d'embarquement\n", + "\n", + "2. Les valeurs manquantes du port d'embarquement sont très peu nombreuses. De plus, s'agissant d'un attribut discret, nous pouvons considérer l'information de valeur nulle comme une valeur possible supplémentaire. Commencer par affichez les lignes pour lesquelles le port d'embarquement n'est pas renseigné." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5219c39a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "363787e8", + "metadata": {}, + "source": [ + "3. Remplacez ces valeurs nulles par la valeur 'U' (pour unknown). Vérifiez vos résultats en réaffichant les lignes obtenues ci-dessus :" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2be8a958", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "a5e22413", + "metadata": {}, + "source": [ + "### Traitement du numéro de cabine\n", + "\n", + "4. Le numéro de cabine est l'attribut qui contient le plus de valeurs nulles. Cet attribut n'a pas de lien évident avec la survie des passagers. Supprimer cette colonne dans votre dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aef1705c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "112f1631", + "metadata": {}, + "source": [ + "### Traitement de l'âge\n", + "5. L'âge est un attribut plus délicat à traiter : il contient un nombre conséquent de valeur nulle, mais il est très pertinent à prendre en compte pour la prédiction de la survie d'un passager, ces deux informations étant assez fortement corrélées. Il existe plein de stratégies pour remplacer ces valeurs manquantes :\n", + "* Mettre une valeur aléatoire '(tirée entre le min et le max)\n", + "* Remplacer par la moyenne\n", + "* Remplacer par une valeur déterminée en fonction des autres paramètres (classe, age, etc)\n", + "\n", + "Commencer par calculer pour chaque genre et pour chaque classe, l'âge moyen (6 valeurs à obtenir au total)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2b2a06ff", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "e42013d7", + "metadata": {}, + "source": [ + "6. Pour chaque ligne du jeu de données, si l'âge est manquant, remplacez la valeur nulle par une des valeurs calculées ci-dessus. basez-vous sur le genre et la classe du passager pour choisir la bonne valeur. " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3daa4cc6", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "cf24aa8b", + "metadata": {}, + "source": [ + "## Préparer les données\n", + "\n", + "Nous entrons dans la dernière phase de traitement des données : nous allons terminer des les mettre en forme, pour qu'elles soient prêtes à être manipulées dans un processus d'apprentissage." + ] + }, + { + "cell_type": "markdown", + "id": "f2ab2605", + "metadata": {}, + "source": [ + "# Encodage des données catégorielles\n", + "\n", + "1. Réaffichez les infos sur le jeu de données. Vous devez avoir 11 colonnes, toutes remplies avec 891 valeurs." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3ac1186f", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "a62f568e", + "metadata": {}, + "source": [ + "2. Trois colonnes sont liées à l'identification unique d'un passager, et ne sont pas pertinentes pour la prédiction de la survie. Supprimez ces trois colonnes de votre jeu de données." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a4b7d99c", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "9126e8f9", + "metadata": {}, + "source": [ + "3. Reproduisez une manipulation vue au module 3 : combinez deux colonnes relatives aux familles de passagers pour n'en faire plus qu'une. Pensez à supprimer les deux anciennes colonnes." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ad70a5d5", + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "a96edd21", + "metadata": {}, + "source": [ + "4. Parmi les colonnes restantes, sur lesquelles un encodage one-hot vous semble pertinent ? En vous appuyant sur [la documentation de scikit-learn](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html), appliquez cet encodage aux colonnes identifiées. Enfin, pensez à supprimer les anciennes colonnes.\n", + "\n", + "Note : pour le genre, il n'y a que deux possibilités dans ce jeu de données. Avec l'option `if_binary` de l'encodeur, vous pouvez ne générer qu'une seule colonne (l'autre s'obtenant immédiatement par déduction)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "1316c770", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "439cb51a", + "metadata": {}, + "source": [ + "5. Vous devez à présent avoir un jeu de données sur 10 attributs, tous numériques (`int64` ou `float64`), sans aucune valeur nulle. Enregistrer ce jeu de données au format csv, afin de pouvoir le réutiliser par la suite. Il est inutile de sauvegarder l'index présent dans le dataframe." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "363a15e3", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "91ccfd1f", + "metadata": {}, + "source": [ + "# Fin du TP !" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/M04_TP01_Solution.ipynb b/M04_TP01_Solution.ipynb new file mode 100644 index 0000000..3807330 --- /dev/null +++ b/M04_TP01_Solution.ipynb @@ -0,0 +1,1387 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "395d61ec", + "metadata": {}, + "source": [ + "# TP1 du module 4 : le travail sur les données.\n", + "\n", + "Dans ce TP, nous allons explorer plus en détails le jeu de données du Titanic. Objectifs :\n", + "* Analyser des statistiques pour décrire les données\n", + "* Produire des visualisations pertinentes pour la compréhesion des données.\n", + "* Nettoyer le jeu de données\n", + "* Préparer les données pour qu'elles soient prêtes à être fournies à un algorithme d'apprentissage." + ] + }, + { + "cell_type": "code", + "id": "5117092f", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-16T10:06:44.308342Z", + "start_time": "2025-09-16T10:06:44.305868Z" + } + }, + "source": [ + "# Ajoutez ici les imports de librairies nécessaires\n", + "import numpy as np\n", + "import pandas as pd\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "from sklearn.preprocessing import OneHotEncoder\n", + "import skitlearn" + ], + "outputs": [], + "execution_count": 4 + }, + { + "cell_type": "markdown", + "id": "fde8da96", + "metadata": {}, + "source": [ + "Commencez par recharger le jeu de données depuis un csv dans un dataframe Pandas. Rappel de l'adresse à laquelle vous pouvez le trouver : https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv" + ] + }, + { + "cell_type": "code", + "id": "33fba6ca", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-16T10:06:44.431049Z", + "start_time": "2025-09-16T10:06:44.315956Z" + } + }, + "source": [ + "titanic = pd.read_csv('https://raw.githubusercontent.com/datasciencedojo/datasets/master/titanic.csv')" + ], + "outputs": [], + "execution_count": 5 + }, + { + "cell_type": "markdown", + "id": "205f765d", + "metadata": {}, + "source": [ + "## Exploration du jeu de données\n", + "\n", + "Commencez par répondre au question suivante. Prenez le temps de bien analyser vos réponses, afin de mieux vous familiariser avec le contenu du jeu de données.\n", + "\n", + "1. Combien de données dans le jeu de données Titanic ?" + ] + }, + { + "cell_type": "code", + "id": "4ee3884e", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-16T10:06:44.443097Z", + "start_time": "2025-09-16T10:06:44.440519Z" + } + }, + "source": [ + "print(\"Nombre de données : \", len(titanic))" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Nombre de données : 891\n" + ] + } + ], + "execution_count": 6 + }, + { + "cell_type": "markdown", + "id": "74f32328", + "metadata": {}, + "source": [ + "2. Combien d'attributs compte le jeu de données ?" + ] + }, + { + "cell_type": "code", + "id": "80eeccc3", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-16T10:06:44.467871Z", + "start_time": "2025-09-16T10:06:44.465137Z" + } + }, + "source": [ + "print(\"Nombre d'attributs : \", len(titanic.columns))" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Nombre d'attributs : 12\n" + ] + } + ], + "execution_count": 7 + }, + { + "cell_type": "markdown", + "id": "819573a7", + "metadata": {}, + "source": [ + "3. Identifiez quelles colonnes contiennent des données discrètes, et lesquelles contiennent des données continues." + ] + }, + { + "cell_type": "code", + "id": "87aa38a1", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-16T10:06:44.497752Z", + "start_time": "2025-09-16T10:06:44.488952Z" + } + }, + "source": [ + "print(titanic.info())" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 891 entries, 0 to 890\n", + "Data columns (total 12 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 PassengerId 891 non-null int64 \n", + " 1 Survived 891 non-null int64 \n", + " 2 Pclass 891 non-null int64 \n", + " 3 Name 891 non-null object \n", + " 4 Sex 891 non-null object \n", + " 5 Age 714 non-null float64\n", + " 6 SibSp 891 non-null int64 \n", + " 7 Parch 891 non-null int64 \n", + " 8 Ticket 891 non-null object \n", + " 9 Fare 891 non-null float64\n", + " 10 Cabin 204 non-null object \n", + " 11 Embarked 889 non-null object \n", + "dtypes: float64(2), int64(5), object(5)\n", + "memory usage: 83.7+ KB\n", + "None\n" + ] + } + ], + "execution_count": 8 + }, + { + "cell_type": "markdown", + "id": "3daaaf5c", + "metadata": {}, + "source": [ + "**Réponse :** \n", + "\n", + "* Données discrètes : PassengerId, Survived, Pclass, Name, Sex, SibSp, Parch, Cabin, Embarked\n", + "* Données continues : Age, Fare, Ticket" + ] + }, + { + "cell_type": "markdown", + "id": "59733c69", + "metadata": {}, + "source": [ + "4. De la même manière, identifiez les colonnes de données qualitatives, et celles de données quantitatives.\n", + "\n", + "**Réponse :** \n", + "\n", + "* Données qualitatives : Survived, Name, Sex, Ticket, Cabin, Embarked\n", + "* Données quantitatives : PassengerId, Pclass, Age, SibSp, Parch, Fare" + ] + }, + { + "cell_type": "markdown", + "id": "69c4bee2", + "metadata": {}, + "source": [ + "5. Affichez les statistiques de base sur les colonnes quantitatives du dataset. \n", + "Quelles informations pouvez-vous en retirer ? Pour chaque attribut, cherchez au moins une information pertinente que vous pouvez déduire de vos observations." + ] + }, + { + "cell_type": "code", + "id": "82ebfbb6", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-16T10:06:44.540637Z", + "start_time": "2025-09-16T10:06:44.518122Z" + } + }, + "source": [ + "titanic.describe()" + ], + "outputs": [ + { + "data": { + "text/plain": [ + " PassengerId Survived Pclass Age SibSp \\\n", + "count 891.000000 891.000000 891.000000 714.000000 891.000000 \n", + "mean 446.000000 0.383838 2.308642 29.699118 0.523008 \n", + "std 257.353842 0.486592 0.836071 14.526497 1.102743 \n", + "min 1.000000 0.000000 1.000000 0.420000 0.000000 \n", + "25% 223.500000 0.000000 2.000000 20.125000 0.000000 \n", + "50% 446.000000 0.000000 3.000000 28.000000 0.000000 \n", + "75% 668.500000 1.000000 3.000000 38.000000 1.000000 \n", + "max 891.000000 1.000000 3.000000 80.000000 8.000000 \n", + "\n", + " Parch Fare \n", + "count 891.000000 891.000000 \n", + "mean 0.381594 32.204208 \n", + "std 0.806057 49.693429 \n", + "min 0.000000 0.000000 \n", + "25% 0.000000 7.910400 \n", + "50% 0.000000 14.454200 \n", + "75% 0.000000 31.000000 \n", + "max 6.000000 512.329200 " + ], + "text/html": [ + "
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" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 9 + }, + { + "cell_type": "markdown", + "id": "a5ad3de1", + "metadata": {}, + "source": [ + "**Observations sur les statistiques :** \n", + "* Il y a malheureusement eu plus de personnes ayant péri que de survivants. La proportion est d'environ 1/3 de survivants.\n", + "* Il semble y avoir eu une majorité (plus de la moitié) de voyageurs en 3e classe.\n", + "* Les âges sont très variés, avec un pic autour de la trentaine.\n", + "* Une majorité de passagers voyageaient sans frère, soeur ou conjoint à bord\n", + "* De même, peu de passagers voyageaient avec des parents ou des enfants.\n", + "* Les prix des tickets semblent très variables, avec une majorité de prix assez faibles et éloignés du prix maximum. Cela est cohérent avec la majorité de voyageurs en 3e classe." + ] + }, + { + "cell_type": "markdown", + "id": "46384afa", + "metadata": {}, + "source": [ + "6. Sur une figure avec 6 sous-figures, proposez un histogramme pour visualiser la répartition des valeurs sur les attributs suivants : Survived, Pclass, Sex, Embarked, Age, Fare. Pour chaque figure, quelle(s) observation(s) pouvez-vous faire ?" + ] + }, + { + "cell_type": "code", + "id": "94ddbac5", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-16T10:06:45.173695Z", + "start_time": "2025-09-16T10:06:44.560697Z" + } + }, + "source": [ + "f, axes = plt.subplots(2, 3, figsize=(20, 10))\n", + " \n", + "# Histogramme pour la survie\n", + "sub1 = axes[0, 0]\n", + "sns.histplot(titanic['Survived'] , color=\"skyblue\", ax=sub1, bins=2)\n", + "sub1.set_xticks([0,1])\n", + "\n", + "# Histogramme pour la classe des passagers\n", + "sub1 = axes[0, 1]\n", + "sns.histplot(titanic['Pclass'] , color=\"skyblue\", ax=sub1, bins=3)\n", + "sub1.set_xticks([1,2,3])\n", + "\n", + "# Histogramme pour le genre des passagers\n", + "sub1 = axes[0, 2]\n", + "sns.histplot(titanic['Sex'] , color=\"skyblue\", ax=sub1, bins=2)\n", + "\n", + "# Histogramme pour le port d'embarquement\n", + "sub1 = axes[1, 0]\n", + "sns.histplot(titanic['Embarked'] , color=\"skyblue\", ax=sub1)\n", + "\n", + "# Histogramme pour l'âge des passagers, regroupés par dizaine\n", + "sub1 = axes[1, 1]\n", + "sns.histplot(titanic['Age'] , color=\"skyblue\", ax=sub1, binwidth=10)\n", + "\n", + "# Histogramme pour le prix du billet\n", + "sub1 = axes[1, 2]\n", + "sns.histplot(titanic['Fare'] , color=\"skyblue\", ax=sub1)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
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+ }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 10 + }, + { + "cell_type": "markdown", + "id": "fd730025", + "metadata": {}, + "source": [ + "**Observations :**\n", + "* Plus de décès que de survivants\n", + "* Majorité de personnes en 3e classe\n", + "* Plus d'hommes que de femmes\n", + "* Grande majorité de personnes ayant embarqué à Southampton\n", + "* Ages très répartis, pic autour de la trentaine (corrobore les statistiques)\n", + "* Beaucoup de billets à bas prix, valeurs très étalées." + ] + }, + { + "cell_type": "markdown", + "id": "620fbbba", + "metadata": {}, + "source": [ + "7. Sur un même graphique, représentez, pour chaque genre, le nombre de personnes ayant survécu. Que pouvez-vous en déduire ? Le genre d'un passager vous parait-il pertinent pour qu'un modèle d'apprentissage puisse prédire si ce passager a survécu ?" + ] + }, + { + "cell_type": "code", + "id": "f58cb499", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-16T10:06:45.449518Z", + "start_time": "2025-09-16T10:06:45.221430Z" + } + }, + "source": [ + "sns.catplot(x =\"Sex\", hue =\"Survived\",\n", + "kind =\"count\", data = titanic)" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 11 + }, + { + "cell_type": "markdown", + "id": "4f61683e", + "metadata": {}, + "source": [ + "**Observation :** Il y avait plus d'hommes que de femmes à bord du bateau, mais les femmes ont été plus nombreuses à survivre. Ainsi, si la majorité des hommes ont péri, chez les femmes la tendance est inversée. Le genre est donc un attribut très pertinent pour prédire la survie ou non, car très discriminant. Il ne sera pas suffisant, mais pourra améliorer la prédiction s'il est combiné à d'autres attributs." + ] + }, + { + "cell_type": "markdown", + "id": "412e2e95", + "metadata": {}, + "source": [ + "8. En vous basant sur une visualisation, observez-vous une corrélation entre certains attributs ? Que pouvez-vous en déduire pour un futur modèle d'apprentissage ?" + ] + }, + { + "cell_type": "code", + "id": "48d8ee62", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-16T10:06:45.719113Z", + "start_time": "2025-09-16T10:06:45.488383Z" + } + }, + "source": [ + "# Filtrer les colonnes numériques, car titanic.corr() échoue sinon.\n", + "# Ajouter des annotations (optionnel mais utile) pour rendre les corrélations lisibles.\n", + "# Commenter ou analyser brièvement la heatmap après coup, pour répondre à \"Que pouvez-vous en déduire\".\n", + "\n", + "# 1. Sélection des colonnes numériques uniquement\n", + "numeric_df = titanic.select_dtypes(include='number')\n", + "\n", + "# 2. Calcul des corrélations\n", + "correlation_matrix = numeric_df.corr()\n", + "\n", + "# 3. Affichage de la heatmap\n", + "sns.heatmap(correlation_matrix, annot=True, cmap='coolwarm')\n", + "plt.title(\"Matrice de corrélation des attributs numériques du Titanic\")\n", + "plt.show()" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 12 + }, + { + "cell_type": "markdown", + "id": "5f9853b3", + "metadata": {}, + "source": [ + "**Observations :**\n", + "* Corrélation entre SibSp et Parch, ce qui est logique étant donné qu'ils sont tous les deux relatifs à la notion de famille\n", + "* Corrélation entre la survie et la classe des passagers : très intéressant pour l'apprentissage\n", + "* Corrélation entre l'âge et la classe des passagers" + ] + }, + { + "cell_type": "markdown", + "id": "ae39b80c", + "metadata": {}, + "source": [ + "9. En vous basant sur vos observations de la visualisation précédente, confirmez vos impressions en proposant deux visualisations. Par exemple, si vous avez observé une corrélation entre un attribut A et un attribut B, mettez en valeur le fait que les mêmes valeurs de A sont souvent trouvées avec les mêmes valeurs de B. " + ] + }, + { + "cell_type": "code", + "id": "987a4b5f", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-16T10:06:45.859230Z", + "start_time": "2025-09-16T10:06:45.727921Z" + } + }, + "source": [ + "# Proposition 1 : survie et classe des passagers\n", + "sns.countplot(data=titanic, x='Pclass',hue='Survived')" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 13 + }, + { + "cell_type": "markdown", + "id": "ae68615d", + "metadata": {}, + "source": [ + "**Observation :** les passagers de classe plus élevée ont eu plus de chance de survie." + ] + }, + { + "cell_type": "code", + "id": "9d66606a", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-16T10:06:46.041959Z", + "start_time": "2025-09-16T10:06:45.865680Z" + } + }, + "source": [ + "# Proposition 2 : âge et classe des passagers\n", + "sns.histplot(\n", + " data=titanic, x='Age', hue='Pclass', multiple='dodge',\n", + " bins=range(1, 110, 10))" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": 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" + }, + "metadata": {}, + "output_type": "display_data", + "jetTransient": { + "display_id": null + } + } + ], + "execution_count": 14 + }, + { + "cell_type": "markdown", + "id": "57cd6075", + "metadata": {}, + "source": [ + "**Observation :** les passagers plus âgées sont souvent dans des classes plus élevées." + ] + }, + { + "cell_type": "markdown", + "id": "67adc89f", + "metadata": {}, + "source": [ + "10. Mettez-vous à présent dans le rôle d'un data analyst face à un nouveau jeu de données : quelles autres visualisations vous paraissent nécessaires ? Proposez au moins trois visualisations qui vous semblent pertinentes. Gardez en tête que l'objectif sur ce jeu de données sera de réussir à prédire si un passager à survécu ou non." + ] + }, + { + "cell_type": "markdown", + "id": "02d7f820", + "metadata": {}, + "source": [ + "**Suggestions de visualisations :**\n", + "* Survie par classe d'âge\n", + "* Survie et nombre de membres de la famille\n", + "* Age et prix du ticket" + ] + }, + { + "cell_type": "markdown", + "id": "081aafe5", + "metadata": {}, + "source": [ + "## Nettoyage des données\n", + "1. Pour chaque colonne, comptez le nombre de valeurs nulles." + ] + }, + { + "cell_type": "code", + "id": "ba42c62a", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-16T10:06:46.062200Z", + "start_time": "2025-09-16T10:06:46.057233Z" + } + }, + "source": [ + "titanic.isna().sum()" + ], + "outputs": [ + { + "data": { + "text/plain": [ + "PassengerId 0\n", + "Survived 0\n", + "Pclass 0\n", + "Name 0\n", + "Sex 0\n", + "Age 177\n", + "SibSp 0\n", + "Parch 0\n", + "Ticket 0\n", + "Fare 0\n", + "Cabin 687\n", + "Embarked 2\n", + "dtype: int64" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 15 + }, + { + "cell_type": "markdown", + "id": "e5bc166d", + "metadata": {}, + "source": [ + "### Traitement du port d'embarquement\n", + "\n", + "2. Les valeurs manquantes du port d'embarquement sont très peu nombreuses. De plus, s'agissant d'un attribut discret, nous pouvons considérer l'information de valeur nulle comme une valeur possible supplémentaire. Commencer par affichez les lignes pour lesquelles le port d'embarquement n'est pas renseigné." + ] + }, + { + "cell_type": "code", + "id": "5219c39a", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-16T10:06:46.092543Z", + "start_time": "2025-09-16T10:06:46.083739Z" + } + }, + "source": [ + "titanic[titanic['Embarked'].isna()]" + ], + "outputs": [ + { + "data": { + "text/plain": [ + " PassengerId Survived Pclass Name \\\n", + "61 62 1 1 Icard, Miss. Amelie \n", + "829 830 1 1 Stone, Mrs. George Nelson (Martha Evelyn) \n", + "\n", + " Sex Age SibSp Parch Ticket Fare Cabin Embarked \n", + "61 female 38.0 0 0 113572 80.0 B28 NaN \n", + "829 female 62.0 0 0 113572 80.0 B28 NaN " + ], + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
616211Icard, Miss. Ameliefemale38.00011357280.0B28NaN
82983011Stone, Mrs. George Nelson (Martha Evelyn)female62.00011357280.0B28NaN
\n", + "
" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 16 + }, + { + "cell_type": "markdown", + "id": "363787e8", + "metadata": {}, + "source": [ + "3. Remplacez ces valeurs nulles par la valeur 'U' (pour unknown). Vérifiez vos résultats en réaffichant les lignes obtenues ci-dessus :" + ] + }, + { + "cell_type": "code", + "id": "2be8a958", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-16T10:06:46.297992Z", + "start_time": "2025-09-16T10:06:46.287441Z" + } + }, + "source": [ + "titanic['Embarked'] = titanic['Embarked'].replace(np.nan, 'U')\n", + "titanic[(titanic['PassengerId']==62) | (titanic['PassengerId']==830)]" + ], + "outputs": [ + { + "data": { + "text/plain": [ + " PassengerId Survived Pclass Name \\\n", + "61 62 1 1 Icard, Miss. Amelie \n", + "829 830 1 1 Stone, Mrs. George Nelson (Martha Evelyn) \n", + "\n", + " Sex Age SibSp Parch Ticket Fare Cabin Embarked \n", + "61 female 38.0 0 0 113572 80.0 B28 U \n", + "829 female 62.0 0 0 113572 80.0 B28 U " + ], + "text/html": [ + "
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PassengerIdSurvivedPclassNameSexAgeSibSpParchTicketFareCabinEmbarked
616211Icard, Miss. Ameliefemale38.00011357280.0B28U
82983011Stone, Mrs. George Nelson (Martha Evelyn)female62.00011357280.0B28U
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" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 17 + }, + { + "cell_type": "markdown", + "id": "a5e22413", + "metadata": {}, + "source": [ + "### Traitement du numéro de cabine\n", + "\n", + "4. Le numéro de cabine est l'attribut qui contient le plus de valeurs nulles. Cet attribut n'a pas de lien évident avec la survie des passagers. Supprimer cette colonne dans votre dataframe." + ] + }, + { + "cell_type": "code", + "id": "aef1705c", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-16T10:06:46.473560Z", + "start_time": "2025-09-16T10:06:46.465828Z" + } + }, + "source": [ + "titanic = titanic.drop('Cabin', axis=1)\n", + "titanic.info()" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 891 entries, 0 to 890\n", + "Data columns (total 11 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 PassengerId 891 non-null int64 \n", + " 1 Survived 891 non-null int64 \n", + " 2 Pclass 891 non-null int64 \n", + " 3 Name 891 non-null object \n", + " 4 Sex 891 non-null object \n", + " 5 Age 714 non-null float64\n", + " 6 SibSp 891 non-null int64 \n", + " 7 Parch 891 non-null int64 \n", + " 8 Ticket 891 non-null object \n", + " 9 Fare 891 non-null float64\n", + " 10 Embarked 891 non-null object \n", + "dtypes: float64(2), int64(5), object(4)\n", + "memory usage: 76.7+ KB\n" + ] + } + ], + "execution_count": 18 + }, + { + "cell_type": "markdown", + "id": "112f1631", + "metadata": {}, + "source": [ + "### Traitement de l'âge\n", + "5. L'âge est un attribut plus délicat à traiter : il contient un nombre conséquent de valeur nulle, mais il est très pertinent à prendre en compte pour la prédiction de la survie d'un passager, ces deux informations étant assez fortement corrélées. Il existe plein de stratégies pour remplacer ces valeurs manquantes :\n", + "* Mettre une valeur aléatoire '(tirée entre le min et le max)\n", + "* Remplacer par la moyenne\n", + "* Remplacer par une valeur déterminée en fonction des autres paramètres (classe, age, etc)\n", + "\n", + "Commencer par calculer pour chaque genre et pour chaque classe, l'âge moyen (6 valeurs à obtenir au total)." + ] + }, + { + "cell_type": "code", + "id": "2b2a06ff", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-16T10:06:46.635521Z", + "start_time": "2025-09-16T10:06:46.627947Z" + } + }, + "source": [ + "age_avg_pclass_sex = titanic.groupby(['Pclass', 'Sex'], as_index=False)['Age'].mean()\n", + "print(age_avg_pclass_sex)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Pclass Sex Age\n", + "0 1 female 34.611765\n", + "1 1 male 41.281386\n", + "2 2 female 28.722973\n", + "3 2 male 30.740707\n", + "4 3 female 21.750000\n", + "5 3 male 26.507589\n" + ] + } + ], + "execution_count": 19 + }, + { + "cell_type": "markdown", + "id": "e42013d7", + "metadata": {}, + "source": [ + "6. Pour chaque ligne du jeu de données, si l'âge est manquant, remplacez la valeur nulle par une des valeurs calculées ci-dessus. basez-vous sur le genre et la classe du passager pour choisir la bonne valeur. " + ] + }, + { + "cell_type": "code", + "id": "3daa4cc6", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-16T10:06:46.716203Z", + "start_time": "2025-09-16T10:06:46.710056Z" + } + }, + "source": [ + "#La méthode transform() est conçue pour conserver l'index d'origine, ce qui permet d’assigner directement le résultat à une colonne du DataFrame.\n", + "#apply() modifie l’index → pas idéal pour affecter une colonne.\n", + "#transform() conserve l’index → parfait pour des remplacements ligne à ligne.\n", + "\n", + "titanic['Age'] = titanic['Age'].groupby([titanic['Pclass'], titanic['Sex']]).transform(lambda x: x.fillna(x.mean()))\n", + "\n", + "#groupby(['Pclass', 'Sex'])['Age'] : groupe les âges selon la classe et le sexe.\n", + "#.transform('mean') : remplace chaque ligne du groupe par la moyenne du groupe, en gardant le même index.\n", + "#.fillna(...) : remplit uniquement les NaN avec la moyenne du groupe correspondant.\n", + "\n", + "# Vérifier que les valeurs manquantes ont bien été remplacées\n", + "print(titanic['Age'].isnull().sum()) # Nombre de NaN restants (devrait être 0 ou très réduit)" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n" + ] + } + ], + "execution_count": 20 + }, + { + "cell_type": "markdown", + "id": "cf24aa8b", + "metadata": {}, + "source": [ + "## Préparer les données\n", + "\n", + "Nous entrons dans la dernière phase de traitement des données : nous allons terminer des les mettre en forme, pour qu'elles soient prêtes à être manipulées dans un processus d'apprentissage." + ] + }, + { + "cell_type": "markdown", + "id": "f2ab2605", + "metadata": {}, + "source": [ + "# Encodage des données catégorielles\n", + "\n", + "1. Réaffichez les infos sur le jeu de données. Vous devez avoir 11 colonnes, toutes remplies avec 891 valeurs." + ] + }, + { + "cell_type": "code", + "id": "3ac1186f", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-16T10:06:46.803405Z", + "start_time": "2025-09-16T10:06:46.796857Z" + } + }, + "source": [ + "titanic.info()" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 891 entries, 0 to 890\n", + "Data columns (total 11 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 PassengerId 891 non-null int64 \n", + " 1 Survived 891 non-null int64 \n", + " 2 Pclass 891 non-null int64 \n", + " 3 Name 891 non-null object \n", + " 4 Sex 891 non-null object \n", + " 5 Age 891 non-null float64\n", + " 6 SibSp 891 non-null int64 \n", + " 7 Parch 891 non-null int64 \n", + " 8 Ticket 891 non-null object \n", + " 9 Fare 891 non-null float64\n", + " 10 Embarked 891 non-null object \n", + "dtypes: float64(2), int64(5), object(4)\n", + "memory usage: 76.7+ KB\n" + ] + } + ], + "execution_count": 21 + }, + { + "cell_type": "markdown", + "id": "a62f568e", + "metadata": {}, + "source": [ + "2. Trois colonnes sont liées à l'identification unique d'un passager, et ne sont pas pertinentes pour la prédiction de la survie. Supprimez ces trois colonnes de votre jeu de données." + ] + }, + { + "cell_type": "code", + "id": "a4b7d99c", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-16T10:06:46.865523Z", + "start_time": "2025-09-16T10:06:46.858478Z" + } + }, + "source": [ + "titanic = titanic.drop(['PassengerId','Name', 'Ticket'], axis=1)\n", + "titanic.info()" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 891 entries, 0 to 890\n", + "Data columns (total 8 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Survived 891 non-null int64 \n", + " 1 Pclass 891 non-null int64 \n", + " 2 Sex 891 non-null object \n", + " 3 Age 891 non-null float64\n", + " 4 SibSp 891 non-null int64 \n", + " 5 Parch 891 non-null int64 \n", + " 6 Fare 891 non-null float64\n", + " 7 Embarked 891 non-null object \n", + "dtypes: float64(2), int64(4), object(2)\n", + "memory usage: 55.8+ KB\n" + ] + } + ], + "execution_count": 22 + }, + { + "cell_type": "markdown", + "id": "9126e8f9", + "metadata": {}, + "source": [ + "3. Reproduisez une manipulation vue au module 3 : combinez deux colonnes relatives aux familles de passagers pour n'en faire plus qu'une. Pensez à supprimer les deux anciennes colonnes." + ] + }, + { + "cell_type": "code", + "id": "ad70a5d5", + "metadata": { + "scrolled": true, + "ExecuteTime": { + "end_time": "2025-09-16T10:06:46.923527Z", + "start_time": "2025-09-16T10:06:46.915421Z" + } + }, + "source": [ + "titanic['Famille'] = titanic['SibSp'] + titanic['Parch']\n", + "titanic = titanic.drop(['SibSp','Parch'], axis=1)\n", + "titanic.info()" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 891 entries, 0 to 890\n", + "Data columns (total 7 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Survived 891 non-null int64 \n", + " 1 Pclass 891 non-null int64 \n", + " 2 Sex 891 non-null object \n", + " 3 Age 891 non-null float64\n", + " 4 Fare 891 non-null float64\n", + " 5 Embarked 891 non-null object \n", + " 6 Famille 891 non-null int64 \n", + "dtypes: float64(2), int64(3), object(2)\n", + "memory usage: 48.9+ KB\n" + ] + } + ], + "execution_count": 23 + }, + { + "cell_type": "markdown", + "id": "a96edd21", + "metadata": {}, + "source": [ + "4. Parmi les colonnes restantes, sur lesquelles un encodage one-hot vous semble pertinent ? En vous appuyant sur [la documentation de scikit-learn](https://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.OneHotEncoder.html), appliquez cet encodage aux colonnes identifiées. Enfin, pensez à supprimer les anciennes colonnes.\n", + "\n", + "Note : pour le genre, il n'y a que deux possibilités dans ce jeu de données. Avec l'option `if_binary` de l'encodeur, vous pouvez ne générer qu'une seule colonne (l'autre s'obtenant immédiatement par déduction)." + ] + }, + { + "cell_type": "code", + "id": "1316c770", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-16T10:06:46.969766Z", + "start_time": "2025-09-16T10:06:46.953407Z" + } + }, + "source": [ + "enc = OneHotEncoder(drop='if_binary')\n", + "\n", + "one_hot = np.array(enc.fit_transform(titanic[['Sex', 'Embarked']]).toarray())\n", + "one_hot_label = enc.get_feature_names_out(['Sex', 'Embarked'])\n", + "\n", + "df = pd.DataFrame(data=one_hot, columns=one_hot_label)\n", + "titanic = titanic.join(df)\n", + "titanic = titanic.drop(['Sex', 'Embarked'], axis=1)\n", + "\n", + "\n", + "titanic.info()" + ], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 891 entries, 0 to 890\n", + "Data columns (total 10 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 Survived 891 non-null int64 \n", + " 1 Pclass 891 non-null int64 \n", + " 2 Age 891 non-null float64\n", + " 3 Fare 891 non-null float64\n", + " 4 Famille 891 non-null int64 \n", + " 5 Sex_male 891 non-null float64\n", + " 6 Embarked_C 891 non-null float64\n", + " 7 Embarked_Q 891 non-null float64\n", + " 8 Embarked_S 891 non-null float64\n", + " 9 Embarked_U 891 non-null float64\n", + "dtypes: float64(7), int64(3)\n", + "memory usage: 69.7 KB\n" + ] + } + ], + "execution_count": 24 + }, + { + "cell_type": "markdown", + "id": "439cb51a", + "metadata": {}, + "source": [ + "5. Vous devez à présent avoir un jeu de données sur 10 attributs, tous numériques (`int64` ou `float64`), sans aucune valeur nulle. Enregistrer ce jeu de données au format csv, afin de pouvoir le réutiliser par la suite. Il est inutile de sauvegarder l'index présent dans le dataframe." + ] + }, + { + "cell_type": "code", + "id": "363a15e3", + "metadata": { + "ExecuteTime": { + "end_time": "2025-09-16T10:06:46.988251Z", + "start_time": "2025-09-16T10:06:46.978468Z" + } + }, + "source": [ + "titanic.to_csv('Titanic.csv', index=False)" + ], + "outputs": [], + "execution_count": 25 + }, + { + "cell_type": "markdown", + "id": "abd641a1-6329-4e31-9625-4428b2d4f6d7", + "metadata": {}, + "source": [ + "### Le fichier est enregistré dans le même dossier que le notebook." + ] + }, + { + "cell_type": "markdown", + "id": "bfacd97a-51a4-4faf-887d-561b1cbfacf7", + "metadata": {}, + "source": [ + "# Fin du TP !" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.7" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/Titanic.csv b/Titanic.csv new file mode 100644 index 0000000..9799097 --- /dev/null +++ b/Titanic.csv @@ -0,0 +1,892 @@ +Survived,Pclass,Age,Fare,Famille,Sex_male,Embarked_C,Embarked_Q,Embarked_S,Embarked_U +0,3,22.0,7.25,1,1.0,0.0,0.0,1.0,0.0 +1,1,38.0,71.2833,1,0.0,1.0,0.0,0.0,0.0 +1,3,26.0,7.925,0,0.0,0.0,0.0,1.0,0.0 +1,1,35.0,53.1,1,0.0,0.0,0.0,1.0,0.0 +0,3,35.0,8.05,0,1.0,0.0,0.0,1.0,0.0 +0,3,26.507588932806325,8.4583,0,1.0,0.0,1.0,0.0,0.0 +0,1,54.0,51.8625,0,1.0,0.0,0.0,1.0,0.0 +0,3,2.0,21.075,4,1.0,0.0,0.0,1.0,0.0 +1,3,27.0,11.1333,2,0.0,0.0,0.0,1.0,0.0 +1,2,14.0,30.0708,1,0.0,1.0,0.0,0.0,0.0 +1,3,4.0,16.7,2,0.0,0.0,0.0,1.0,0.0 +1,1,58.0,26.55,0,0.0,0.0,0.0,1.0,0.0 +0,3,20.0,8.05,0,1.0,0.0,0.0,1.0,0.0 +0,3,39.0,31.275,6,1.0,0.0,0.0,1.0,0.0 +0,3,14.0,7.8542,0,0.0,0.0,0.0,1.0,0.0 +1,2,55.0,16.0,0,0.0,0.0,0.0,1.0,0.0 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